# K-Core based Temporal Graph Convolutional Network for Dynamic Graphs

arXiv: https://arxiv.org/abs/2003.09902 · HTML: https://mlsanity.com/paper/2003.09902/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [CGC: Contrastive Graph Clustering for Community Detection and Tracking](https://mlsanity.com/paper/2204.08504/) (2022)
- [ConTIG: Continuous Representation Learning on Temporal Interaction Graphs](https://mlsanity.com/paper/2110.06088/) (2021)
- [Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs](https://mlsanity.com/paper/2210.00032/) (2022)
- [Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction](https://mlsanity.com/paper/2110.03875/) (2021)
- [UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs](https://mlsanity.com/paper/2502.16431/) (2025)
- [Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network](https://mlsanity.com/paper/2301.08399/) (2023)
- [TEGDetector: A Phishing Detector that Knows Evolving Transaction Behaviors](https://mlsanity.com/paper/2111.15446/) (2021)
- [LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning](https://mlsanity.com/paper/2311.16605/) (2023)
- [Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2408.02704/) (2024)
- [A Novel Spatiotemporal Coupling Graph Convolutional Network](https://mlsanity.com/paper/2408.07087/) (2024)
- [Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding](https://mlsanity.com/paper/2503.19926/) (2025)
- [WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features](https://mlsanity.com/paper/2608.00642/) (2026)
- [HAWKEYE: Seeing One Layer Deeper -- A Cohesion-Aware Structural Channel for Temporal Link Prediction](https://mlsanity.com/paper/2608.22727/) (2026)

## Abstract (arXiv)

Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution is generalized into two phases, feature transformation and feature aggregation, which gives the CTGCN more flexibility and enables the CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate that the CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, including link prediction and structural role classification. The source code of this work can be obtained from urlhttps://github.com/jhljx/CTGCN.